Marine ecosystems produce heterogeneous data from satellites, buoys, research vessels, fisheries activity, laboratory sequencing, and environmental DNA (eDNA) sampling. These sources can reveal ocean conditions, fish distribution, ecosystem change, and biodiversity, but are often stored in separate systems with incompatible formats and metadata. Artificial intelligence (AI) can support forecasting, anomaly detection, habitat modelling, fisheries analysis, and molecular classification. This review examines deep learning for marine water-quality assessment, wireless sensor networks, hybrid models for aquaculture, and molecular biodiversity workflows. It proposes a conceptual unified platform with secure ingestion, source-aware storage, metadata harmonisation, data fusion, AI analytics, and decision-support dashboards. Major challenges include uneven sampling, data interoperability, sensor drift, uncertain labels, model transferability, computational cost, and governance of sensitive fisheries and biodiversity data. The platform is a review-derived architecture rather than an implemented or experimentally validated system.
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Raju et al. (2026) studied this question.
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